concrete.ml.quantization.base_quantized_op
module concrete.ml.quantization.base_quantized_op
concrete.ml.quantization.base_quantized_op
Base Quantized Op class that implements quantization for a float numpy op.
Global Variables
ONNX_OPS_TO_NUMPY_IMPL
ALL_QUANTIZED_OPS
ONNX_OPS_TO_QUANTIZED_IMPL
DEFAULT_MODEL_BITS
class QuantizedOp
QuantizedOp
Base class for quantized ONNX ops implemented in numpy.
Args:
n_bits_output
(int): The number of bits to use for the quantization of the outputint_input_names
(Set[str]): The set of names of integer tensors that are inputs to this opconstant_inputs
(Optional[Union[Dict[str, Any], Dict[int, Any]]]): The constant tensors that are inputs to this opinput_quant_opts
(QuantizationOptions): Input quantizer options, determine the quantization that is applied to input tensors (that are not constants)
method __init__
__init__
property op_type
Get the type of this operation.
Returns:
op_type
(str): The type of this operation, in the ONNX referential
method calibrate
calibrate
Create corresponding QuantizedArray for the output of the activation function.
Args:
*inputs (numpy.ndarray)
: Calibration sample inputs.
Returns:
numpy.ndarray
: the output values for the provided calibration samples.
method call_impl
call_impl
Call self.impl to centralize mypy bug workaround.
Args:
*inputs (numpy.ndarray)
: real valued inputs.**attrs
: the QuantizedOp attributes.
Returns:
numpy.ndarray
: return value of self.impl
method can_fuse
can_fuse
Determine if the operator impedes graph fusion.
This function shall be overloaded by inheriting classes to test self._int_input_names, to determine whether the operation can be fused to a TLU or not. For example an operation that takes inputs produced by a unique integer tensor can be fused to a TLU. Example: f(x) = x * (x + 1) can be fused. A function that does f(x) = x * (x @ w + 1) can't be fused.
Returns:
bool
: whether this instance of the QuantizedOp produces Concrete Numpy code that can be fused to TLUs
classmethod must_quantize_input
must_quantize_input
Determine if an input must be quantized.
Quantized ops and numpy onnx ops take inputs and attributes. Inputs can be either constant or variable (encrypted). Note that this does not handle attributes, which are handled by QuantizedOp classes separately in their constructor.
Args:
input_name_or_idx
(int): Index of the input to check.
Returns:
result
(bool): Whether the input must be quantized (must be aQuantizedArray
) or if it stays as a rawnumpy.array
read from ONNX.
method prepare_output
prepare_output
Quantize the output of the activation function.
The calibrate method needs to be called with sample data before using this function.
Args:
qoutput_activation
(numpy.ndarray): Output of the activation function.
Returns:
QuantizedArray
: Quantized output.
method q_impl
q_impl
Execute the quantized forward.
Args:
*q_inputs (QuantizedArray)
: Quantized inputs.**attrs
: the QuantizedOp attributes.
Returns:
QuantizedArray
: The returned quantized value.
class QuantizedOpUnivariateOfEncrypted
QuantizedOpUnivariateOfEncrypted
An univariate operator of an encrypted value.
This operation is not really operating as a quantized operation. It is useful when the computations get fused into a TLU, as in e.g. Act(x) = x || (x + 42)).
method __init__
__init__
property op_type
Get the type of this operation.
Returns:
op_type
(str): The type of this operation, in the ONNX referential
method calibrate
calibrate
Create corresponding QuantizedArray for the output of the activation function.
Args:
*inputs (numpy.ndarray)
: Calibration sample inputs.
Returns:
numpy.ndarray
: the output values for the provided calibration samples.
method call_impl
call_impl
Call self.impl to centralize mypy bug workaround.
Args:
*inputs (numpy.ndarray)
: real valued inputs.**attrs
: the QuantizedOp attributes.
Returns:
numpy.ndarray
: return value of self.impl
method can_fuse
can_fuse
Determine if this op can be fused.
This operation can be fused and computed in float when a single integer tensor generates both the operands. For example in the formula: f(x) = x || (x + 1) where x is an integer tensor.
Returns:
bool
: Can fuse
classmethod must_quantize_input
must_quantize_input
Determine if an input must be quantized.
Quantized ops and numpy onnx ops take inputs and attributes. Inputs can be either constant or variable (encrypted). Note that this does not handle attributes, which are handled by QuantizedOp classes separately in their constructor.
Args:
input_name_or_idx
(int): Index of the input to check.
Returns:
result
(bool): Whether the input must be quantized (must be aQuantizedArray
) or if it stays as a rawnumpy.array
read from ONNX.
method prepare_output
prepare_output
Quantize the output of the activation function.
The calibrate method needs to be called with sample data before using this function.
Args:
qoutput_activation
(numpy.ndarray): Output of the activation function.
Returns:
QuantizedArray
: Quantized output.
method q_impl
q_impl
Execute the quantized forward.
Args:
*q_inputs (QuantizedArray)
: Quantized inputs.**attrs
: the QuantizedOp attributes.
Returns:
QuantizedArray
: The returned quantized value.
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